Published By: Dell EMC
Published Date: Feb 14, 2019
Isilon scale-out NAS delivers the analytics performance and extreme concurrency at scale to feed the most data hungry analytic algorithms. Access this overview from Dell and Intel® to learn more.
Intel Inside®. Powerful Productivity Outside.
On-demand companies rely on fast, accurate and robust mapping and location technologies to provide their users with a superior experience. Find out how real-time, predictive and historical traffic data can be applied to traffic-enabled routing algorithms to influence route calculations and automatically plot multiple routes with waypoints sequencing.
Discover how HERE can help you communicate updated ETAs and provide an optimized experience to your drivers and customers.
Blockchain in Financial Services is receiving a lot of attention, especially for synchronizing financial agreements between institutions. But how can blockchain be used outside of this context? Can it apply to use cases such as identity, fraud, and AML?
Watch this short webinar to hear how blockchain can be used to solve other key issues facing the industry, about research into consensus algorithms beyond proof of work, and about myths and truths that must be considered for a successful enterprise blockchain implementation.
Speaker: Nelson Petracek, CTO, TIBCO Software
MoneyLIVE’s annual survey of over 600 banking professionals found that traditional banks face a significant challenge when it comes to building AI-powered customer journeys.
75% believe that as the use of AI intensifies, banks will struggle to recruit the necessary expertise.
84% fear regulatory and liability issues surrounding AI.
Just 7% think their organization’s use of AI is highly sophisticated.
But for banks to keep pace with challengers and FinTechs, it’s crucial that they harness this continually evolving technology.
Download this chapter of MoneyLIVE's The Future of Retail Banking Report 2018/19 now and understand how TIBCO’s Connected Intelligence Platform, with the use of AI and machine learning algorithms, can help with banks’ digital transformation needs.
The idea of load balancing is well defined in the IT world: A network device accepts traffic on behalf ofa group of servers, and distributes that traffic according to load balancing algorithms and the availabilityof the services that the servers provide. From network administrators to server administrators to applicationdevelopers, this is a generally well understood concept.
The NSA’s Information Assurance Directorate left many people scratching their heads in the winter
of 2015. The directive instructed those that follow its guidelines to postpone moving from RSA
cryptography to elliptic curve cryptography (ECC) if they hadn’t already done so.
“For those partners and vendors that have not yet made the transition to Suite B elliptic curve
algorithms, we recommend not making a significant expenditure to do so at this point but instead to
prepare for the upcoming quantum-resistant algorithm transition.”
The timing of the announcement was curious. Many in the crypto community wondered if there had been
a quantum computing breakthrough significant enough to warrant the NSA’s concern. A likely candidate
for such a breakthrough came from the University of New South Wales, Australia, where researchers
announced that they’d achieved quantum effects in silicon, which would be a massive jump forward for
Komprimierungsalgorithmen sorgen dafür, dass weniger Bit benötigt werden, um einen bestimmten Datensatz zu repräsentieren. Je höher das Komprimierungsverhältnis, desto mehr Speicherplatz wird durch dieses spezielle Datenreduzierungsverfahren eingespart. Während unseres OLTP-Tests erreichte das Unity-Array bei den Datenbank-Volumes ein Komprimierungsverhältnis von 3,2:1, während das 3PAR-Array im Schnitt nur ein Verhältnis von 1,3:1 erreichte. In unserem Data Mart-Ladetest erzielte das 3PAR bei den Datenbank-Volumes ein Verhältnis von 1,4:1, das Unity-Array nur 1,3:1.
The transition to autonomous is all around. Its capability for problem-solving has never been seen before. Its potential for creating business value from algorithms and data makes it the next big frontier for business leaders. Two industry experts have discussed Oracle Autonomous Data Warehouse Cloudand what it can help organisations achieve. Talking about innovation,
security and efficiency, they put the casefor an autonomous future.
Published By: FusionOps
Published Date: Jun 15, 2016
The supply chain generates huge volumes of data captured in ERP, CRM, demand planning and other systems. Download this whitepaper to learn how FusionOps Machine Learning can provide companies with a more accurate, granular understanding of their business by harmonizing these disparate data sources in the cloud, and applying machine learning algorithms.
In some cases, adopting cloud IoT platform may make more sense where required processes, communication costs and cloud costs meet sufficient total cost of ownership against deploying MDC. Additionally, in situations that an end-user organization already has a secure room or a modular data center solution where infrastructure can be housed and/or the amount of infrastructure involved may be too small to benefit from power/cooling advantages of being housed in an MDC, the organization may not see a need for an MDC. An MDC is nothing more than a smaller form of a modular data center, and a number of providers have entered the modular data center solutions space in the past. These modular data center solution providers came into the market with high expectations for growth and ROI only to find that high sales were not forthcoming due to limited use cases, so many exited the space.
Compression algorithms reduce the number of bits needed to represent a set of data—the higher the compression ratio, the more space this particular data reduction technique saves. During our OLTP test, the Unity array achieved a compression ratio of 3.2-to-1 on the database volumes, whereas the 3PAR array averaged a 1.3-to-1 ratio. In our data mart loading test, the 3PAR achieved a ratio of 1.4-to-1 on the database volumes, whereas the Unity array got 1.3 to 1.
Les algorithmes de compression réduisent le nombre de bits nécessaires pour représenter un ensemble de données. Plus le taux de compression est élevé, plus cette technique de réduction des données permet d’économiser de l’espace. Lors de notre test OLTP, la baie Unity a atteint un taux de compression de 3,2 pour 1 sur les volumes de base de données. De son côté, la baie 3PAR affichait en moyenne un taux de 1,3 pour 1. Sur le test de chargement DataMart, la baie 3PAR a atteint un taux de 1,4 pour 1 sur les volumes de bases de données, tandis que la baie Unity enregistrait un taux de 1,3 pour 1.
Predictive analytics have been used by different industries for years to solve difficult problems that range from detecting credit card fraud to determining patient risk levels for medical conditions. It combines data mining and machine-learning technologies to create statistical models based on historical data. It then uses these models to predict future events. Extracting the power from the data requires powerful algorithms behind predictive analytics.
Published By: Clustrix
Published Date: Sep 04, 2013
Find out how AdScience has been able to increase their revenue potential by five times using Clustrix to optimize bidding for their online ad broker agency. AdScience runs complicated algorithms to process bids for ad space based on click history. It's critical for AdScience to have instant access to smart data.
The misuse or takeover of privileged accounts constitutes the most common source of breaches today. CA Threat Analytics for PAM provides a continuous, intelligent monitoring capability that helps enterprises detect and stop hackers and malicious insiders before they cause damage.
The software integrates a powerful set of user behavior analytics and machine learning algorithms with the trusted controls provided by CA Privileged Access Manager (CA PAM). The result is a solution that continuously analyzes the activity of individual users, accurately detects malicious and high-risk activities and automatically triggers mitigating controls to limit damage to the enterprise.
Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled. Neural networks are one type of model for machine learning; they have been around
Published By: Monetate
Published Date: Oct 11, 2018
Monetate Intelligent Recommendations is the only solution that gives merchandisers & digital marketers the power to show contextually relevant product recommendations without burdening IT resources.
Using manually curated or algorithmically-driven recommendations, marketers can easily support even the most complex product catalogs. Our solution filters recommendations based on customer attributes (e.g. shirt size), longitudinal behaviours (e.g. browsing behaviour), and situational context (e.g. product inventory at local stores). Best of all, an orchestration layer intelligently selects which algorithms and which filters to apply in any given situation, for any particular individual.
Published By: Monetate
Published Date: Oct 22, 2018
Monetate Intelligent Recommendations automates recommendations at scale without sacrificing any of the control you require. Our proprietary algorithms know what to serve each individual shopper to maximise brand value, while still allowing the control of an unlimited number of business guardrails defined by you.
Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.
Générez des données virtuelles riches qui couvrent tous les scénarios possibles et fournissent un accès illimité aux environnements nécessaires pour livrer des applications testées avec soin, dans les délais et le budget impartis. Modélisez les données des systèmes réels complexes et appliquez des algorithmes d’apprentissage automatisé de règles pour éliminer la dette technique et permettre une compréhension approfondie des applications composites. Mettez en outre à la disposition des équipes distribuées des données virtuelles à la demande et évitez les goulots d’étranglement au niveau des tests.
In this digital world, fast and reliable movement of digital
data, including massive sizes over global distances, is
becoming vital to business success across virtually every
industry. The Transmission Control Protocol (TCP) that has
traditionally been the engine of this data movement, however, has
inherent bottlenecks in performance (Figure 1), especially for
networks with high, round-trip time (RTT) and packet loss, and most
pronounced on high-bandwidth networks. It is well understood that
these inherent “soft” bottlenecks arcaused by TCP’s AdditiveIncrease-Multiplicative-Decrease (AIMD) congestion avoidance
algorithm, which slowly probes the available bandwidth of the
network, increasing the transmission rate until packet loss is detected
and then exponentially reducing the transmission rate. However, it is
less understood that other sources of packet loss, such as losses due
to the physical network media, not associated with network
congestion equally reduce the transmission rate.
Generate rich virtual data that covers the full range of possible scenarios and provide the unconstrained access to environments needed to deliver rigorously tested applications on time and within budget. Model complex live system data and apply automated rule-learning algorithms to pay off technical debt and uncover in depth understanding of composite applications, while exposing virtual data to distributed teams on demand and avoiding testing bottlenecks.
The transition to autonomous is all around. Its capability for problem-solving has never been seen before. Its potential for creating business value from algorithms and data makes it the next big frontier for business leaders. Two industry experts have discussed Oracle Autonomous Data Warehouse Cloud and what it can help organisations achieve. Talking about innovation,security and efficiency, they put the case for an autonomous future.
Watch the webinar.
Published By: MobileIron
Published Date: Aug 20, 2018
MobileIron knows that cybercriminals are continuously generating more advanced ways to steal your data by any means necessary. That’s why we are committed to continually innovating and delivering new solutions that help our customers win the race against time to get ahead of the latest mobile security threats. As part of that commitment, MobileIron Threat Defense supports the five critical steps to deploying advanced, on-device mobile security. Our solution provides a single, integrated app that delivers several key advantages:
• A single app of threat protection is fully integrated with EMM.
• No user action is required to activate or update on-device security.
• Advanced mobile security blocks known and zero-day threats across iOS and Android devices with no Internet connectivity required.
• Machine-learning algorithms instantly detect and remediate on-device DNA threats.
Between the Internet of Things, customer experience and loyalty programs, social network monitoring, connected enterprise systems and other information sources, today's organizations have access to more data than they ever had before-and frankly, more than they may know what to do with. The challenge is to not just understand that data, but actualize it and use it to recognize real business value. This ebook will walk you through a sample scenario with Albert, a data scientist who wants to put text analytics to work by using the Word2vec algorithm and other data science tools.